The hippocampus is an essential structure for remembering personal events that have occurred in unique spatial and temporal contexts (episodic memory). The long-term objective of this project is to understand how detailed hippocampal circuits (i.e., subfields) and associated cortical areas process event-related information and to establish a causal relationship between these neural processes and mnemonic behavior. Advanced recording techniques and sophisticated analytical methods currently allow monitoring of the firing patterns of a large number of neurons simultaneously from multiple brain regions in freely behaving animals. With respect to episodic memory, however, the functional significance of the knowledge gained by using these techniques remains unclear, because the neural activity is rarely recorded when the animals are engaged in an episodic-like memory task. This project will fill this critical gap and elucidate how different networks of neurons in the hippocampus contribute to the animal's behavior when context-specific events need to be encoded and retrieved. Computational models predict that the dentate gyrus and CAS subfields of the hippocampus are essential when independent representations of different contexts need to be formed (pattern separation) and also when original representations of contexts need to be retrieved in the face of incomplete/ambiguous contexts (pattern completion). This proposal will test these critical predictions from the computational models by adopting an innovative investigative strategy. Specifically, the animals will be required to produce discrete behavioral responses in association with different contextual environments. Furthermore, this proposal seeks to identify the relationship between these context-dependent, behavioral responses and the ensemble neuronal activity recorded from multiple hippocampal subfields simultaneously. The outcomes from the current project will thus advance our knowledge of how episodic memory components are processed in detailed hippocampal circuits by linking the predictions from computational theories, physiological properties of neural networks, and mnemonic behavior. Clinically, damage within the hippocampus and associated areas (e.g., in Alzheimer's disease and schizophrenia) results in a devastating amnesic syndrome. The outcomes from this proposal will thus contribute to creating improved diagnosis and treatment of the hippocampal-dependent amnesia.